



import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


lowerCAmelCase__	    =    logging.get_logger(__name__)

class  snake_case__(_snake_case     ):

					"""simple docstring"""






					lowercase_								=						["""input_features""", """is_longer"""]







					def __init__(				self   :      Optional[int] ,  SCREAMING_SNAKE_CASE   :      Dict=64 ,  SCREAMING_SNAKE_CASE   :      int=48_000 ,  SCREAMING_SNAKE_CASE   :      Optional[Any]=480 ,  SCREAMING_SNAKE_CASE   :      List[str]=10 ,  SCREAMING_SNAKE_CASE   :      Tuple=1_024 ,  SCREAMING_SNAKE_CASE   :      Union[str, Any]=0.0 ,  SCREAMING_SNAKE_CASE   :      str=False ,  SCREAMING_SNAKE_CASE   :      Tuple = 0 ,  SCREAMING_SNAKE_CASE   :      Any = 14_000 ,  SCREAMING_SNAKE_CASE   :      str = None ,  SCREAMING_SNAKE_CASE   :      Tuple = "fusion" ,  SCREAMING_SNAKE_CASE   :      List[str] = "repeatpad" ,  **SCREAMING_SNAKE_CASE   :      str ,  ):
									super().__init__(
									    feature_size=__snake_case ,  sampling_rate=__snake_case ,  padding_value=__snake_case ,  return_attention_mask=__snake_case ,  **__snake_case ,  )
									lowercase__	:       Dict						=	top_db
									lowercase__	:       List[str]						=	truncation
									lowercase__	:       Dict						=	padding
									lowercase__	:       Optional[int]						=	fft_window_size
									lowercase__	:       List[str]						=	(fft_window_size >> 1) + 1
									lowercase__	:       Tuple						=	hop_length
									lowercase__	:       Optional[Any]						=	max_length_s
									lowercase__	:       Union[str, Any]						=	max_length_s * sampling_rate
									lowercase__	:       str						=	sampling_rate
									lowercase__	:       Optional[int]						=	frequency_min
									lowercase__	:       Union[str, Any]						=	frequency_max
									lowercase__	:       str						=	mel_filter_bank(
									    num_frequency_bins=self.nb_frequency_bins ,  num_mel_filters=__snake_case ,  min_frequency=__snake_case ,  max_frequency=__snake_case ,  sampling_rate=__snake_case ,  norm=__snake_case ,  mel_scale="htk" ,  )
									lowercase__	:       int						=	mel_filter_bank(
									    num_frequency_bins=self.nb_frequency_bins ,  num_mel_filters=__snake_case ,  min_frequency=__snake_case ,  max_frequency=__snake_case ,  sampling_rate=__snake_case ,  norm="slaney" ,  mel_scale="slaney" ,  )







					def 					snake_case	(				self   :      List[str]	):
									lowercase__	:       Any						=	copy.deepcopy(self.__dict__	)
									lowercase__	:       Dict						=	self.__class__.__name__
									if "mel_filters" in output:
													del output["mel_filters"]
									if "mel_filters_slaney" in output:
													del output["mel_filters_slaney"]
									return output







					def 					snake_case	(				self   :      Any ,  SCREAMING_SNAKE_CASE   :      Any ,  SCREAMING_SNAKE_CASE   :      Optional[int] = None	):
									lowercase__	:       List[Any]						=	spectrogram(
									    __snake_case ,  window_function(self.fft_window_size ,  "hann"	) ,  frame_length=self.fft_window_size ,  hop_length=self.hop_length ,  power=2.0 ,  mel_filters=__snake_case ,  log_mel="dB" ,  )
									return log_mel_spectrogram.T







					def 					snake_case	(				self   :      Optional[int] ,  SCREAMING_SNAKE_CASE   :      Optional[Any] ,  SCREAMING_SNAKE_CASE   :      List[str] ,  SCREAMING_SNAKE_CASE   :      List[Any]	):
									lowercase__	:       Optional[int]						=	np.array_split(list(range(0 ,  total_frames - chunk_frames + 1	)	) ,  3	)
									if len(ranges[1]	) == 0:
													# if the audio is too short, we just use the first chunk
													lowercase__	:       List[Any]						=	[0]
									if len(ranges[2]	) == 0:
													# if the audio is too short, we just use the first chunk
													lowercase__	:       Optional[Any]						=	[0]
									# randomly choose index for each part
									lowercase__	:       List[str]						=	np.random.choice(ranges[0]	)
									lowercase__	:       Dict						=	np.random.choice(ranges[1]	)
									lowercase__	:       Optional[int]						=	np.random.choice(ranges[2]	)

									lowercase__	:       List[Any]						=	mel[idx_front : idx_front + chunk_frames, :]
									lowercase__	:       int						=	mel[idx_middle : idx_middle + chunk_frames, :]
									lowercase__	:       List[Any]						=	mel[idx_back : idx_back + chunk_frames, :]

									lowercase__	:       Dict						=	torch.tensor(mel[None, None, :]	)
									lowercase__	:       Optional[Any]						=	torch.nn.functional.interpolate(
									    __snake_case ,  size=[chunk_frames, 64] ,  mode="bilinear" ,  align_corners=__snake_case	)
									lowercase__	:       Optional[int]						=	mel_shrink[0][0].numpy()
									lowercase__	:       str						=	np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] ,  axis=0	)
									return mel_fusion







					def 					snake_case	(				self   :      Tuple ,  SCREAMING_SNAKE_CASE   :      Dict ,  SCREAMING_SNAKE_CASE   :      Optional[int] ,  SCREAMING_SNAKE_CASE   :      Any ,  SCREAMING_SNAKE_CASE   :      int	):
									if waveform.shape[0] > max_length:
													if truncation == "rand_trunc":
																	lowercase__	:       List[str]						=	True
																	# random crop to max_length (for compatibility) -> this should be handled by self.pad
																	lowercase__	:       str						=	len(__snake_case	) - max_length
																	lowercase__	:       List[Any]						=	np.random.randint(0 ,  overflow + 1	)
																	lowercase__	:       int						=	waveform[idx : idx + max_length]
																	lowercase__	:       int						=	self._np_extract_fbank_features(__snake_case ,  self.mel_filters_slaney	)[None, :]
													elif truncation == "fusion":
																	lowercase__	:       Union[str, Any]						=	self._np_extract_fbank_features(__snake_case ,  self.mel_filters	)
																	lowercase__	:       Union[str, Any]						=	max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
																	lowercase__	:       str						=	mel.shape[0]
																	if chunk_frames == total_frames:
																					# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
																					# In this case, we just use the whole audio.
																					lowercase__	:       Optional[Any]						=	np.stack([mel, mel, mel, mel] ,  axis=0	)
																					lowercase__	:       List[str]						=	False
																	else:
																					lowercase__	:       str						=	self._random_mel_fusion(__snake_case ,  __snake_case ,  __snake_case	)
																					lowercase__	:       Tuple						=	True
													else:
																	raise NotImplementedError(f"""data_truncating {truncation} not implemented"""	)

									else:
													lowercase__	:       Union[str, Any]						=	False
													# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
													if waveform.shape[0] < max_length:
																	if padding == "repeat":
																					lowercase__	:       Optional[int]						=	int(max_length / len(__snake_case	)	)
																					lowercase__	:       List[str]						=	np.stack(np.tile(__snake_case ,  n_repeat + 1	)	)[:max_length]
																	if padding == "repeatpad":
																					lowercase__	:       List[str]						=	int(max_length / len(__snake_case	)	)
																					lowercase__	:       Dict						=	np.stack(np.tile(__snake_case ,  __snake_case	)	)
																	lowercase__	:       Any						=	np.pad(__snake_case ,  (0, max_length - waveform.shape[0]) ,  mode="constant" ,  constant_values=0	)

													if truncation == "fusion":
																	lowercase__	:       Optional[Any]						=	self._np_extract_fbank_features(__snake_case ,  self.mel_filters	)
																	lowercase__	:       Any						=	np.stack([input_mel, input_mel, input_mel, input_mel] ,  axis=0	)
													else:
																	lowercase__	:       Optional[int]						=	self._np_extract_fbank_features(__snake_case ,  self.mel_filters_slaney	)[None, :]

									return input_mel, longer







					def __call__(				self   :      Optional[Any] ,  SCREAMING_SNAKE_CASE   :      Tuple ,  SCREAMING_SNAKE_CASE   :      Optional[int] = None ,  SCREAMING_SNAKE_CASE   :      Union[str, Any] = None ,  SCREAMING_SNAKE_CASE   :      Dict = None ,  SCREAMING_SNAKE_CASE   :      Tuple = None ,  SCREAMING_SNAKE_CASE   :      List[str] = None ,  **SCREAMING_SNAKE_CASE   :      Tuple ,  ):
									lowercase__	:       Optional[int]						=	truncation if truncation is not None else self.truncation
									lowercase__	:       Dict						=	padding if padding else self.padding

									if sampling_rate is not None:
													if sampling_rate != self.sampling_rate:
																	raise ValueError(
																	    f"""The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"""
																	    f""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
																	    f""" was sampled with {self.sampling_rate} and not {sampling_rate}."""	)
									else:
													logger.warning(
													    "It is strongly recommended to pass the `sampling_rate` argument to this function. "
													    "Failing to do so can result in silent errors that might be hard to debug."	)

									lowercase__	:       Optional[Any]						=	isinstance(__snake_case ,  np.ndarray	) and len(raw_speech.shape	) > 1
									if is_batched_numpy and len(raw_speech.shape	) > 2:
													raise ValueError(f"""Only mono-channel audio is supported for input to {self}"""	)
									lowercase__	:       List[str]						=	is_batched_numpy or (
									    isinstance(__snake_case ,  (list, tuple)	) and (isinstance(raw_speech[0] ,  (np.ndarray, tuple, list)	))
									)

									if is_batched:
													lowercase__	:       Optional[int]						=	[np.asarray(__snake_case ,  dtype=np.floataa	) for speech in raw_speech]
									elif not is_batched and not isinstance(__snake_case ,  np.ndarray	):
													lowercase__	:       str						=	np.asarray(__snake_case ,  dtype=np.floataa	)
									elif isinstance(__snake_case ,  np.ndarray	) and raw_speech.dtype is np.dtype(np.floataa	):
													lowercase__	:       Dict						=	raw_speech.astype(np.floataa	)

									# always return batch
									if not is_batched:
													lowercase__	:       str						=	[np.asarray(__snake_case	)]

									# convert to mel spectrogram, truncate and pad if needed.
									lowercase__	:       Union[str, Any]						=	[
									    self._get_input_mel(__snake_case ,  max_length if max_length else self.nb_max_samples ,  __snake_case ,  __snake_case	)
									    for waveform in raw_speech
									]

									lowercase__	:       List[str]						=	[]
									lowercase__	:       Optional[Any]						=	[]
									for mel, longer in padded_inputs:
													input_mel.append(__snake_case	)
													is_longer.append(__snake_case	)

									if truncation == "fusion" and sum(__snake_case	) == 0:
													# if no audio is longer than 10s, then randomly select one audio to be longer
													lowercase__	:       List[Any]						=	np.random.randint(0 ,  len(__snake_case	)	)
													lowercase__	:       str						=	True

									if isinstance(input_mel[0] ,  __snake_case	):
													lowercase__	:       int						=	[np.asarray(__snake_case ,  dtype=np.floataa	) for feature in input_mel]

									# is_longer is a list of bool
									lowercase__	:       List[str]						=	[[longer] for longer in is_longer]

									lowercase__	:       Dict						=	{"""input_features""": input_mel, """is_longer""": is_longer}
									lowercase__	:       List[str]						=	BatchFeature(__snake_case	)

									if return_tensors is not None:
													lowercase__	:       Optional[Any]						=	input_features.convert_to_tensors(__snake_case	)

									return input_features








'''simple docstring'''


import math







def      snake_case_  (    SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Tuple							= math.loga(math.sqrt(4 * positive_integer + 1	) / 2 + 1 / 2	)

				return exponent == int(SCREAMING_SNAKE_CASE__	)







def      snake_case_  (    SCREAMING_SNAKE_CASE__ = 1 / 1_2345	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			str							= 0
				_SCREAMING_SNAKE_CASE					:			str							= 0

				_SCREAMING_SNAKE_CASE					:			int							= 3
				while True:
								_SCREAMING_SNAKE_CASE					:			Optional[int]							= (integer**2 - 1) / 4
								# if candidate is an integer, then there is a partition for k
								if partition_candidate == int(SCREAMING_SNAKE_CASE__	):
												_SCREAMING_SNAKE_CASE					:			int							= int(SCREAMING_SNAKE_CASE__	)
												total_partitions += 1
												if check_partition_perfect(SCREAMING_SNAKE_CASE__	):
																perfect_partitions += 1
								if perfect_partitions > 0:
												if perfect_partitions / total_partitions < max_proportion:
																return int(SCREAMING_SNAKE_CASE__	)
								integer += 1


if __name__ == "__main__":
				print(F"{solution() = }")




